Overview

Dataset statistics

Number of variables9
Number of observations2000
Missing cells1660
Missing cells (%)9.2%
Duplicate rows1256
Duplicate rows (%)62.8%
Total size in memory140.8 KiB
Average record size in memory72.1 B

Variable types

NUM8
BOOL1

Warnings

Dataset has 1256 (62.8%) duplicate rows Duplicates
BloodPressure has 90 (4.5%) missing values Missing
SkinThickness has 573 (28.6%) missing values Missing
Insulin has 956 (47.8%) missing values Missing
BMI has 28 (1.4%) missing values Missing
Pregnancies has 301 (15.0%) zeros Zeros

Reproduction

Analysis started2020-09-07 14:59:39.582410
Analysis finished2020-09-07 14:59:48.504680
Duration8.92 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Pregnancies
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7035
Minimum0
Maximum17
Zeros301
Zeros (%)15.0%
Memory size15.6 KiB
2020-09-07T20:29:48.580465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.306063033
Coefficient of variation (CV)0.8926861166
Kurtosis0.409867576
Mean3.7035
Median Absolute Deviation (MAD)2
Skewness0.9823655943
Sum7407
Variance10.93005278
MonotocityNot monotonic
2020-09-07T20:29:48.673217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
135617.8%
 
030115.0%
 
228414.2%
 
31959.8%
 
41919.6%
 
51417.0%
 
61316.6%
 
71005.0%
 
8964.8%
 
9703.5%
 
Other values (7)1356.8%
 
ValueCountFrequency (%) 
030115.0%
 
135617.8%
 
228414.2%
 
31959.8%
 
41919.6%
 
ValueCountFrequency (%) 
1730.1%
 
1520.1%
 
1470.4%
 
13221.1%
 
12231.1%
 

Glucose
Real number (ℝ≥0)

Distinct135
Distinct (%)6.8%
Missing13
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean121.9753397
Minimum44
Maximum199
Zeros0
Zeros (%)0.0%
Memory size15.6 KiB
2020-09-07T20:29:48.783920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile80
Q199
median117
Q3141
95-th percentile181
Maximum199
Range155
Interquartile range (IQR)42

Descriptive statistics

Standard deviation30.63288283
Coefficient of variation (CV)0.2511399673
Kurtosis-0.3598027486
Mean121.9753397
Median Absolute Deviation (MAD)20
Skewness0.5113063262
Sum242365
Variance938.3735104
MonotocityNot monotonic
2020-09-07T20:29:48.903600image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
99492.5%
 
100442.2%
 
102391.9%
 
129371.8%
 
106361.8%
 
112361.8%
 
95361.8%
 
105341.7%
 
120331.7%
 
111331.7%
 
Other values (125)161080.5%
 
ValueCountFrequency (%) 
4420.1%
 
5630.1%
 
5750.2%
 
6130.1%
 
6220.1%
 
ValueCountFrequency (%) 
19930.1%
 
19820.1%
 
19780.4%
 
19650.2%
 
19580.4%
 

BloodPressure
Real number (ℝ≥0)

MISSING

Distinct46
Distinct (%)2.4%
Missing90
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean72.40366492
Minimum24
Maximum122
Zeros0
Zeros (%)0.0%
Memory size15.6 KiB
2020-09-07T20:29:49.039263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile52
Q164
median72
Q380
95-th percentile90
Maximum122
Range98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.22796763
Coefficient of variation (CV)0.1688860315
Kurtosis0.9011263224
Mean72.40366492
Median Absolute Deviation (MAD)8
Skewness0.2101691174
Sum138291
Variance149.5231924
MonotocityNot monotonic
2020-09-07T20:29:49.167894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%) 
741457.2%
 
701447.2%
 
781286.4%
 
681256.2%
 
641206.0%
 
721185.9%
 
80984.9%
 
62944.7%
 
76934.7%
 
60924.6%
 
Other values (36)75337.6%
 
(Missing)904.5%
 
ValueCountFrequency (%) 
2420.1%
 
3030.1%
 
3830.1%
 
4020.1%
 
44110.5%
 
ValueCountFrequency (%) 
12230.1%
 
11430.1%
 
11070.4%
 
10850.2%
 
10690.4%
 

SkinThickness
Real number (ℝ≥0)

MISSING

Distinct52
Distinct (%)3.6%
Missing573
Missing (%)28.6%
Infinite0
Infinite (%)0.0%
Mean29.3412754
Minimum7
Maximum110
Zeros0
Zeros (%)0.0%
Memory size15.6 KiB
2020-09-07T20:29:49.289568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile13
Q122
median29
Q336
95-th percentile46
Maximum110
Range103
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.80227753
Coefficient of variation (CV)0.368159781
Kurtosis5.810887312
Mean29.3412754
Median Absolute Deviation (MAD)7
Skewness1.081610466
Sum41870
Variance116.6891998
MonotocityNot monotonic
2020-09-07T20:29:49.409248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
32834.2%
 
30753.8%
 
23603.0%
 
27582.9%
 
28542.7%
 
18542.7%
 
39522.6%
 
33512.5%
 
31502.5%
 
25472.4%
 
Other values (42)84342.1%
 
(Missing)57328.6%
 
ValueCountFrequency (%) 
730.1%
 
860.3%
 
10130.7%
 
11140.7%
 
12211.1%
 
ValueCountFrequency (%) 
11020.1%
 
9920.1%
 
6330.1%
 
6020.1%
 
5920.1%
 

Insulin
Real number (ℝ≥0)

MISSING

Distinct181
Distinct (%)17.3%
Missing956
Missing (%)47.8%
Infinite0
Infinite (%)0.0%
Mean153.743295
Minimum14
Maximum744
Zeros0
Zeros (%)0.0%
Memory size15.6 KiB
2020-09-07T20:29:49.529951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile42
Q176.75
median126
Q3190
95-th percentile391.25
Maximum744
Range730
Interquartile range (IQR)113.25

Descriptive statistics

Standard deviation111.2736394
Coefficient of variation (CV)0.7237625509
Kurtosis4.483723806
Mean153.743295
Median Absolute Deviation (MAD)54
Skewness1.890093303
Sum160508
Variance12381.82282
MonotocityNot monotonic
2020-09-07T20:29:49.645616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
105311.6%
 
140241.2%
 
180231.1%
 
130221.1%
 
120211.1%
 
100201.0%
 
76170.9%
 
135170.9%
 
94170.9%
 
66150.8%
 
Other values (171)83741.9%
 
(Missing)95647.8%
 
ValueCountFrequency (%) 
1430.1%
 
1530.1%
 
1630.1%
 
1850.2%
 
2230.1%
 
ValueCountFrequency (%) 
74420.1%
 
68020.1%
 
60020.1%
 
57940.2%
 
54520.1%
 

BMI
Real number (ℝ≥0)

MISSING

Distinct246
Distinct (%)12.5%
Missing28
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean32.65010142
Minimum18.2
Maximum80.6
Zeros0
Zeros (%)0.0%
Memory size15.6 KiB
2020-09-07T20:29:49.909909image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.2
Q127.5
median32.4
Q336.8
95-th percentile45.2
Maximum80.6
Range62.4
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation7.241027268
Coefficient of variation (CV)0.2217765628
Kurtosis2.784217688
Mean32.65010142
Median Absolute Deviation (MAD)4.7
Skewness0.9290429988
Sum64386
Variance52.43247589
MonotocityNot monotonic
2020-09-07T20:29:50.031626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
31.2331.7%
 
32331.7%
 
31.6291.5%
 
33.3271.4%
 
32.8251.2%
 
32.4251.2%
 
32.9241.2%
 
30.8241.2%
 
30.1221.1%
 
29.7221.1%
 
Other values (236)170885.4%
 
(Missing)281.4%
 
ValueCountFrequency (%) 
18.280.4%
 
18.420.1%
 
19.120.1%
 
19.330.1%
 
19.420.1%
 
ValueCountFrequency (%) 
80.620.1%
 
67.130.1%
 
64.420.1%
 
59.430.1%
 
57.330.1%
 

DiabetesPedigreeFunction
Real number (ℝ≥0)

Distinct505
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47093
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Memory size15.6 KiB
2020-09-07T20:29:50.155279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.141
Q10.244
median0.376
Q30.624
95-th percentile1.136
Maximum2.42
Range2.342
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.3235525587
Coefficient of variation (CV)0.687050217
Kurtosis5.006839839
Mean0.47093
Median Absolute Deviation (MAD)0.168
Skewness1.811978894
Sum941.86
Variance0.1046862582
MonotocityNot monotonic
2020-09-07T20:29:50.279919image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.258160.8%
 
0.207150.8%
 
0.268130.7%
 
0.292130.7%
 
0.261130.7%
 
0.238130.7%
 
0.52130.7%
 
0.284120.6%
 
0.551120.6%
 
0.259120.6%
 
Other values (495)186893.4%
 
ValueCountFrequency (%) 
0.07820.1%
 
0.08420.1%
 
0.08550.2%
 
0.08860.3%
 
0.08920.1%
 
ValueCountFrequency (%) 
2.4230.1%
 
2.32920.1%
 
2.13730.1%
 
1.89320.1%
 
1.78120.1%
 

Age
Real number (ℝ≥0)

Distinct52
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.0905
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Memory size15.6 KiB
2020-09-07T20:29:50.401593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q340
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.78642311
Coefficient of variation (CV)0.3561875193
Kurtosis0.8263829494
Mean33.0905
Median Absolute Deviation (MAD)7
Skewness1.181267223
Sum66181
Variance138.9197696
MonotocityNot monotonic
2020-09-07T20:29:50.523268image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
221929.6%
 
211668.3%
 
251346.7%
 
241226.1%
 
231035.1%
 
28984.9%
 
26844.2%
 
27814.0%
 
29703.5%
 
31582.9%
 
Other values (42)89244.6%
 
ValueCountFrequency (%) 
211668.3%
 
221929.6%
 
231035.1%
 
241226.1%
 
251346.7%
 
ValueCountFrequency (%) 
8130.1%
 
7230.1%
 
7030.1%
 
6960.3%
 
6830.1%
 

Outcome
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
0
1316 
1
684 
ValueCountFrequency (%) 
0131665.8%
 
168434.2%
 
2020-09-07T20:29:50.607044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Interactions

2020-09-07T20:29:39.983241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:40.106939image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:40.217643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:40.324863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:40.438559image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:40.566218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:40.689888image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:40.816663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:40.942811image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:41.086384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:41.227009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:41.389243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:41.507948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:41.630021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:41.745117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:41.866188image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:42.011947image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:42.139427image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:42.260204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:42.372957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:42.486706image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:42.610451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:42.724210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:42.859892image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:42.992536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:43.112247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:43.217959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:43.317692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:43.417425image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:43.521148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:43.627862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:43.737578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:43.842263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:43.952994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:44.101569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:44.242193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:44.373876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:44.494550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:44.613200image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:44.748872image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:44.874501image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:44.995179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:45.377157image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:45.494854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:45.610534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:45.724230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:45.827953image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:45.937660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:46.056342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:46.177019image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:46.296699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:46.417377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:46.540073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:46.646788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:46.743528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:46.858196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:46.963938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:47.081630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:47.193336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:47.298052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:47.403763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:47.516461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:47.637148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:47.746850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-07T20:29:50.671872image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-07T20:29:50.834436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-07T20:29:50.997001image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-07T20:29:51.149593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-09-07T20:29:47.945313image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:48.125279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:48.276841image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-07T20:29:48.397953image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
02138.062.035.0NaN33.60.127471
1084.082.031.0125.038.20.233230
20145.0NaNNaNNaN44.20.630311
30135.068.042.0250.042.30.365241
41139.062.041.0480.040.70.536210
50173.078.032.0265.046.51.159580
6499.072.017.0NaN25.60.294280
78194.080.0NaNNaN26.10.551670
8283.065.028.066.036.80.629240
9289.090.030.0NaN33.50.292420

Last rows

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
19903111.090.012.078.028.40.495290
19916102.082.0NaNNaN30.80.180361
19926134.070.023.0130.035.40.542291
1993287.0NaN23.0NaN28.90.773250
1994179.060.042.048.043.50.678230
1995275.064.024.055.029.70.370330
19968179.072.042.0130.032.70.719361
1997685.078.0NaNNaN31.20.382420
19980129.0110.046.0130.067.10.319261
1999281.072.015.076.030.10.547250

Duplicate rows

Most frequent

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcomecount
146281.072.015.076.030.10.5472506
480173.078.032.0265.046.51.1595805
148283.065.028.066.036.80.6292405
2594125.070.018.0122.028.91.1444515
3086154.074.032.0193.029.30.8393905
3307195.070.033.0145.025.10.1635515
3084.082.031.0125.038.20.2332304
150102.064.046.078.040.60.4962104
300126.084.029.0215.030.70.5202404
360135.068.042.0250.042.30.3652414